This study used field measurements of CO2 fluxes from control and vegetation removal plots to estimate ecosystem respiration, heterotrophic respiration (HR), and autotrophic respiration (AR) in an ombrotrophic bog ecosystem over two growing seasons. The study analyzed the correlations of temperature and water table with respiration fluxes for the two years. The sensitivity of different respiration fluxes to environmental factors is an important question with implications for understanding ecosystem carbon flux responses to changing climate, as is well explained in the Introduction. I thought the study was well designed and produced a valuable dataset for understanding these fluxes and their controls in bog ecosystems.

Thank you for your comments.

In my opinion the statistical analysis portion of the study had some weaknesses that could be addressed. 

We will address each of the comments in order.

First, some of the statistical methods are not explained in enough detail in the methods section. In particular, it’s not clear how the “multiple regression trees” were conducted or how this method was defined. A full explanation and/or citation for that method would be helpful. 

In the revised manuscript, we will insert more details and explanations of the statistical methods that we used. 

Second, the statistical methods rely on linear regressions. Moisture interactions with respiration in particular are often nonlinear (a threshold dependence is suggested in the Discussion, for example) so I would recommend testing whether linear relationships are an appropriate model for the processes of interest and, if not, applying nonlinear methods where appropriate. 

We will discuss this in the revised manuscript.  We did test for linear and non-linear relationships over the range of our empirical observed data.  The relationships were linear within that range and therefore appropriate for this particular project.  We will also point out that others have found non-linear relationships with a different range of data. 

Third, it’s not clear why the two years were analyzed separately instead of combined as a single dataset. Since it was all the same site and treatments, it would make sense to treat the whole time series as a common dataset and potentially this would give the overall statistical analysis more power. While it is interesting to see if some relationships differed across years, I think a good default assumption would be that the site should behave similarly in different years unless there is a compelling reason to expect otherwise. I suggest conducting the statistical analysis for the whole dataset across both years and perhaps contrasting those results with analyses for individual years if there are significant differences.

We will discuss this in the revised manuscript, but we looked at the relationships across two different years. However, the years were significantly different, and it raises the question if the data can be treated as being from the same population.  

Finally, the results of the statistical analysis that are present are very limited. Only statistical significance metrics, coefficients of variation, and R2 values are shown. This means that the manuscript never reports the direction or slope of the linear relationships and therefore leaves out a lot of potentially useful information. Statistical significance measures on their own are much less informative if they are not matched with information on how the relationships actually looked. I would recommend at minimum including the linear regression parameters (slope and intercept) in a table. Even more useful would be scatter plots with regression lines showing the data and fit relationships for fluxes and environmental factors (especially if some of the relationships were particularly interesting or significant). Overall, it seems like the study generated a useful dataset but did not fully analyze it.

We will include in the revised manuscript a table of the additional regression parameters.  We did scatter plots as part of the analysis and if the editor feels these add useful information we can include them in a supplemental document.  We did not include them in the body of the article or as supplemental material originally as we felt they did not add significantly to the results presented. 

Other comments:

Line 63-68: This explanation of “plant-mediated HR” did not make sense to me. First it is explained as plants fixing carbon that was recently respired from surrounding vegetation. This isn’t HR, it’s reabsorption of respired CO2. And I don’t see why this is a problem for calculating ER. From the perspective of ecosystem carbon balance, it shouldn't matter if the carbon source for photosynthesis came from ecosystem respiration or from the atmosphere — aren’t they all carbon molecules in the end? Does it make a difference how far they traveled? Later, plant-mediated HR is explained as having to do with root-soil interactions and litter supply, which seems like a different issue from reabsorption of respired CO2. A different process that could be called “plant-mediated HR” is supply of C to the rhizosphere that is immediately respired by heterotrophic organisms. This explanation is more consistent with the Discussion paragraph on this topic, which is mostly about rhizosphere priming effects. This does seem like an issue for partitioning AR and HR because it is plant-supplied C that would be cut off by removing plants but it is not strictly AR. But this does not fit with the explanation of “plant-mediated HR” in the Introduction text.

We will discuss that there are three sources of CO2 belowground for which we cannot discriminate: CO2 that is supplied as a substrate by the vascular plants (priming effect), root respiration itself, and heterotrophic respiration by microbial bacteria, etc. that is not associated with the roots. 

Instead of using the term “plant-mediated HR'', we’ll discuss respiration more as an association of CO2 with the structure of the peat. For example, with regards to the mosses, we have recycled C as CO2 that is refixed by the mosses to be used in photosynthesis. We will revise the manuscript accordingly to clarify this. 

Line 123-125: The wording here sounds like the vegetation removal happened under dark conditions, but I think what is meant is that CO2 flux was only measured under dark conditions (not light conditions) in plots where vegetation or mosses were removed. Not that the vegetation removal itself was done in the dark.

We will change the wording in the methods to make it clear that removal was done first then CO2 fluxes were measured under dark conditions.

Line 124: Plots with mosses removed are later referred to as “shrub-only plots.” The same terminology should be used throughout the manuscript.

We will make sure to use the same terminology throughout.

Line 209: The text says that ER and HR were correlated with air and soil temperatures, but based on Table 2 soil T was only significant in one year.

We will make sure to be clear in which year the significant relationships were found.

Line 247: Were the influences positive or negative? And how strong? Only providing statistical significance measures and nothing else leaves out the most important information here

Although it was mentioned in lines 213-215 if the influences were positive or negative, we agree that there was no mention of the strength of these relationships. As stated above, we will include in the table some of the other statistical parameters to show the strength of the relationships. 

Line 225: Again, knowing that this interaction was significant is less useful than knowing what the relationship looked like.

Figure 5 can be broken up into two years with a line showing the average in AR contributions so that the variation is more evident, which is actually a suggestion made in another comment below. We agree with this suggestion and will revise the figure as such as well as refer back to this figure in line 225 to make the variations in AR easier to see.

Line 265: The relative influences of soil T and water table on fluxes could be determined from the parameters of the multiple regressions rather than speculating about it  based on qualitative looks from the figures as this sentence does.

We will refer back to the statistics when explaining the influences of the environmental variables.

Line 274-275: The relative contributions of AR to ER under different conditions could be shown directly with a scatter plot of the relevant processes, or by referring to parameters of the linear regressions.

As there are already figures of the time series of weather conditions and AR contributions, adding additional scatter plots will not add much to the paper. We will include, though, evidence from the statistical analyses in the text to give more credence to the claims.

Line 276-277: If there is a real statistical connection between AR and environmental drivers, then why would higher variability in environmental drivers cause the relationship to be weaker? Might this suggest that the apparent relationship is due to some other covariate that varies more slowly over the year? Or that respiration responds to environmental drivers at a particular time scale?

You make a good point here! However, with the limited sample size in AR fluxes, the relationship, as it would have been, may not have been captured properly. It may be that the respiration responds at a different time scale than our study period. The way to resolve this would be to use continuous measurements (e.g. automatic chambers), which we do not have for this study. We will revise accordingly. 

Line 278-280: A threshold relationship with WT could be shown directly with a scatter plot of WT versus respiration. Also, a threshold response is inherently nonlinear which suggests that linear regression may not provide an accurate picture of the relationship.

We will add a scatterplot as a supplemental figure as stated above, but the relationships were linear within the range of our observed data, so we felt that linear regressions were appropriate.

Line 304: It seems speculative to talk about symbiotic relationships here. The data don’t have enough detail to say whether there is a symbiotic component to the observed correlations.

We will move away from using the word “symbiosis” and explain instead that there is a possibility of the mosses and vascular plants having a mutual benefit to one another by their presence in the ecosystem (the vascular plants provide a source of CO2 that may diffuse through the mosses, while the mosses provide moisture in the water they retain during extended periods of drought). 

We do believe with the data we’ve obtained that this is a valid possibility. But, we will revise the manuscript to be more clear that this is a speculation and that we’re not making a conclusive statement. 

Line 305-309: This should be in the results section

 Agreed, we shall move up to the results section and simply refer to the table in the text here.

Line 315-317: This should be in the results section

Agreed, we shall move up to the results section and simply refer to the table in the text here.

Line 322: Wouldn’t this be an interaction term in the multiple regression? The regression would indicate whether the interaction term was significant or not. And conducting the statistics across both years instead of separately by year could give better statistical power.

Indeed the multiple regressions would tell us whether the interaction term was significant or not, but since 2018 was an anomalous year in terms of weather conditions, we believe lumping the data from the two years will only give a spurious relationship. 

Figure 1: I think it would be helpful to superimpose continuous measurements of temperature and water table (as lines) along with the dots showing values when fluxes were measured. This would allow those time points to be placed in the context of the whole time series.

The time series in Figure 1 shows values for the environmental variables taken at the same time as the flux measurements, so the continuous measurements were added as an appendix mainly to contextualize the manual measurements. They do correlate though, if we look at the values on the same day between the manual and continuous measurements. We can add a graph to the appendix to show this if the editor and reviewers think it would be helpful.

Figures 3 and 4: I found these plots difficult to read with all the different colored dots. Connecting the dots with lines or plotting as bars rather than dots might make these figures easier to interpret.

We will try various ways of reporting the fluxes so the figures in the revised manuscript will be easier to interpret. 

Figure 5: This figure should have separate panels for the two years (similar to the previous figures) or show one long time series. Plotting them on top of each other makes the plot difficult to read.

Agreed, as stated above. This will be done for the revised manuscript as well as including the average AR contributions so the variation is more prominent.

Table 2 and 3: The bold and italics notation for different years is difficult to read, especially since the order of years is not consistent. Also, there’s no reason not to show all the data. These tables should just have a line for each year (two lines per environmental variable) and show all the values (whether statistically significant or not). And, ideally, include statistics over both years of combined data. Also, the regression parameters (slope(s) and intercept) should be included.

We will include the regression parameters and will separate the two years of data in the table. Adding all of the non-significant data though may make the table too busy and we feel it won’t add to the paper as these values won’t be discussed in the text.

This manuscript presents data from a field experiment where CO2 fluxes were measured in control, complete vegetation removal, and moss removal plots in an ombrotrophic bog in order to estimate ecosystem respiration. Further, the vegetation removal treatments were used to partition respiration into contributions from autotrophic respiration and heterotrophic respiration. Measurements were conducted across two growing seasons, and respiration measurements were coupled with measurements of environmental variables such as water table height and air and soil temperatures in order to identify drivers of respiration across the growing season and among different vegetation types.

While the objectives of this study and the rich dataset are valuable contributions to the field, I agree with many points made by Reviewer 1 in addressing the statistical weaknesses of this paper. I find six key points that warrant attention on behalf of the authors to improve the strength of this paper’s analyses and conclusions.

We thank the reviewer for their comments and suggestions. We will address each one in turn below.

• The structure of the discussion is rather confusing. Perhaps separating the discussion section into environmental predictors of AR vs. environmental predictors of HR, temporal variability in AR and HR, and vegetation type differences in respiration would make for a more succinct discussion that directly relates to your manuscript’s stated objectives.

As AR is a residual term (difference between ER and HR), and AR is hence dependent on HR, we believe that separating the environmental predictors of AR and HR into two sections is not a favourable option. Perhaps re-wording the section headings though, and moving up the last paragraph of section 4.1 to right after line 254, would make the flow better?

• As Reviewer 1 suggests, a clearer definition of the methods used as part of the “multiple regression trees'' is necessary. Further, I suggest instead using model comparison and selection methods like stepwise AIC comparison of models to identify the suite of variables that best explain HR and AR in bog areas dominated by different vegetation types. This would better allow you to identify the most predictive combination of variables in this system.

As was stated in the reply to reviewer 1, the authors will provide a clearer definition of the methods used, especially with regards to the multiple regression trees. The authors thank the reviewer for the additional suggestions. We will look into conducting the stepwise AIC and will add the results in the revised manuscript if necessary.

• I disagree with the author’s discussion of “plant mediated HR” in this manuscript. In the introduction, the author’s define plant mediated HR as photosynthesis conducted using CO2 respired by surrounding plants instead of CO2 sourced from ambient pools. This variable is not measured at any point in this study and would require isotopic analyses of plant biomass, assuming that plant mediated HR results in significant fractionation of C isotopes so that photosynthate from plant mediated HR would bear a distinct isotopic signature than would photosynthate from ambient sources. While the authors postulate many credible theories as to why the presence of mosses and the functional differences between shrubs and sedges might alter the physical and chemical properties that influence respiration, these ideas should instead be discussed in a section that is dedicated to describing differences in respiration among vegetation types, eliminating the rather confusing term “plant mediated HR”.

As stated in the reply to reviewer 1 comments, instead of using the term “plant-mediated HR'', we will discuss respiration more as an association of CO2 with the structure of the peat. For example, with regard to the mosses, we have recycled C as CO2 that is refixed by the mosses to be used in photosynthesis. We will revise the manuscript accordingly to clarify this. 

We will also discuss that there are three sources of CO2 belowground which we cannot discriminate: CO2 that is supplied as a substrate by the vascular plants (priming effect), root respiration itself, and heterotrophic respiration by microbial bacteria, etc. that is not associated with the roots. 

• As Reviewer 1 mentioned, the results that the authors report are compelling but insufficient to give readers a clear understanding of how the environmental variables measured here influence respiration. The tables and manuscript text should be amended to include correlation coefficients that report the magnitude and direction of the relationships analyzed in this manuscript, and all results should be reported regardless of whether or not the relationships are statistically significant. Insignificant results are interesting too! Other aspects of the tables are confusing as well. Instead of including 2018 and 2019 data in the same columns with different font faces to differentiate them, consider including separate columns for each year (unless you choose to analyze data from both years together, as suggested by Reviewer 1). I also don’t understand what the second row of data under some environmental variable labels (i.e. row 2 of data in Table 2) refers to. Table structure must be amended in all tables in this manuscript to improve clarity.

The authors will revise the tables to improve clarity and as stated in the reply to reviewer 1 comments, we will include in the revised manuscript a table of the additional regression parameters and will separate the two years of data in the table. Adding all of the non-significant data though may make the table too busy and we feel it will not add to the paper as these values will not be discussed in the text. We also drew scatter plots as part of the analysis, and if the editor feels these add useful information, we can include them in a supplemental document. 

• The figures in this manuscript are often visually unclear or confusing. In Figure 2, the colors used to indicate drying vs. rewetting points are virtually identical and extremely difficult to differentiate. Perhaps change the size, color, and transparency of the points in this figure to allow readers to see differences near the asymptotes where many points are stacked on top of one another. In Figures 3 and 4, the colors for ER and NEE are also too similar to distinguish, especially when considering that the figures would be much smaller in the final published article.  It is also difficult to distinguish between the blue colors used in Figure 5 for the shrub plots.

We will make all the figures clearer with regards to size and color.

• In Figure 5, why not include error bars for AR contribution data points as the authors did in Figures 3 and 4? While connecting the points with lines across the growing season would help readers distinguish temporal trends in AR contributions among your treatments, I suggest averaging AR contributions in each plot across the growing season and then visualizing differences in AR contributions among growing season years and vegetation types using boxplots. These differences can then be verified using an ANOVA test. 

As AR is a residual term, we did not think it was possible to include error bars nor to conduct an ANOVA test, but will add the error bars and results of ANOVA test if this is possible. 

• For Table 2, I would prefer to see panels of linear regressions that depict the relationships between respiration components and environmental variables. This table of statistical results can then be moved into the appendix.

Reviewer 1 also suggested this and as stated in the reply, we felt that adding all of the non-significant data would make the table too busy and will not add to the paper as these values will not be discussed in the text, but we can include them in a supplemental document along with correlations.

• An important spatial component of bogs that this manuscript largely ignores is the hummock/hollow variation in microtopography.  I would suggest reframing the objectives of this study as analyzing temporal/vegetative variation in bog respiration dynamics to reflect your experimental design more accurately.

We examined patterns of respiration in hummocks, which represent 70% of the bog, and incorporated mosses, shrubs and sedges. 

Specific line comments:

Line 121: How much time elapsed between the removal of plant biomass and the installation of root exclosures and the first CO2 flux measurements? Were vegetation removal treatments reapplied throughout the two years of measurements?

We will explain this more clearly in the text. 

Line 182: Because hysteresis does exist to some degree, and the amount of hysteresis varies among years, why not use VWC measurements as your variable that represents soil moisture conditions instead of WT height?

We do not have SWC measurements for the different treatments, only the data from the probes near the eddy covariance tower. We could show that the relationship between WTD and SWC are correlated though, and that WTD is thus a reasonable surrogate for changes in SWC, though it is different because of the hysteresis present. 

Lines 202-208: Be consistent when reporting p-values. I tend to see 3 decimal places for p-values reported, with exact values used instead of simply reporting significance thresholds.

We will revise accordingly.

Line 216: There’s a small typo here, “mater” should probably be “water”.

We will revise accordingly.

Line 222: I do not think that you have the evidence to support your claim that variation in rain events (sporadic rain events) drives greater variation in AR among vegetation types. Furthermore, throughout this paragraph, you should report the coefficient of variation more accurately instead of rounding, as well as p-values and F-statistics stemming from an ANOVA that should be used to properly test the differences in AR contributions among vegetation types or among years. Furthermore, reporting your degrees of freedom associated with the F-statistic in these analyses would help the readers understand how many independent measurements are used in your analyses.

We will revise accordingly regarding the reporting of the statistics. Our comments on the impact of sporadic rain events were speculative and we will make it clear that we are not claiming a cause-effect relationship.

Lines 231-235: This paragraph is unnecessary given the use of subheadings in your discussion.

We will revise accordingly.

Lines 240-243: Perhaps remove reference to Moore et al. 2002 and Stewart et al. 2006 because these studies are not directly comparable to your results given differences in measurement methodology, which you note.

We will revise accordingly. 

Line 305: When you say “importance of 70%”, what is the statistic that you are reporting here, and from which statistical test is this number derived?

Perhaps the word “explanation” should have been used here instead of “importance”? We will revise accordingly.

Lines 322-330: As Reviewer 1 stated in their comments, the relationship between the environmental variables and respiration components discussed in this paragraph likely stem from non-linear relationships between respiration and soil moisture in particular. Using statistical tests beyond linear regressions would be a more appropriate way to test this hypothesis.

As stated in the reply to reviewer 1 comments, we will discuss this in the revised manuscript. We did test for linear and non-linear relationships over the range of our data. The relationships were linear within that range and therefore appropriate for this particular project. We will also point out that others have found non-linear relationships with a different range of data. 

Line 338: Other studies such as Rewcastle et al. 2020 (Pedosphere) use different methods of root exclosures that eliminate the possibility of CO2 flux stemming from residual root decomposition, yet also find rather variable HR rates owing to water table and soil moisture differences irrespective of bog microtopography differences.

The authors thank the reviewer for the suggested citation and we will include it in the paper, although they are dealing with a forested bog rather than a shrub-dominated bog like Mer Bleu. 

Line 331-348: As in other sections of this manuscript, the results that you report must be more specific. Report exact p-values instead of significance, and report p-values even for insignificant results. Results from regressions should include correlation coefficients as well, and results from ANOVA tests should include F-statistics with degrees of freedom to communicate replication in your study.

We will revise accordingly.

Line 354: My understanding of the literature surrounding bog decomposition suggests the opposite, that the high degree of secondary compounds in moss litter inhibits microbial activity, while vascular plant litter and root exudates often have a priming effect on microbial activity in bog ecosystems. Evapotranspiration surrounding vascular plants might also increase oxygen availability by lowering the water table in proximity to deeply-rooted plants, again stimulating microbial activity (further supporting the pattern observed by Zeh et al. 2020).

The other papers cited do suggest what we are arguing, although we have no way of confirming our suggestion, so we will present the alternative (Zeh’s paper) as a contrast. 

Line 375: I would suggest referencing a study other than Hungate et al. 1997 that confirms this ecological principle in bogs rather than grasslands owing to the complex physio-chemical regulation of the carbon cycle in frequently water-saturated ecosystems like bogs.

We will search for a more recent study that was conducted in a bog and the authors thank the reviewer for the suggestion.